PNNL Careers

Atmospheric Data Analyst

RICHLAND, WA


Job Description

Job ID: 308075
Full/Part Time: Full-Time
Regular/Temporary: Regular

Job Description

Pacific Northwest National Laboratory (PNNL) is recruiting an early career scientist interested in developing new scientific insights into the initiation, organization, dynamics, and microphysics of convective cloud populations. You will be expected to work as part of a team of scientists investigating convective clouds, with a long-term goal of using new observational, theoretical and modeling insights to improve representation of convection and microphysical processes in both high resolution regional and global models. Specifically, you will be expected to apply modern statistical and computational methods to analyze large data-sets from observations (e.g. radar, satellite) and models (LES, regional-scale, global climate) to understand cloud-environment interactions that lead to convective transitions. Two-way interactions of microphysical processes with convective circulations such as updrafts, downdrafts, and cold pools on fine spatiotemporal scales are of particular interest. The ability of multi-scale models and variable complexity physics parameterizations to capture observed cloud and environmental processes will be critical in predicting the convective life cycle. You will identify sources of model biases through innovative model sensitivity experiments design and evaluation.

An ability to analyze large observational (e.g. radar, satellite) and model (LES, regional-scale, global climate) datasets and to pose hypotheses that tease out new insights from the large datasets is critical. Using data assimilation techniques to constrain environmental conditions in models and better represent cloud distributions is desirable. In collaboration with others on the team, you may also contribute to analyzing the ability of models to reproduce the observed impacts of convective clouds and precipitation on surface properties, aerosol redistribution, and boundary layer evolution with feedbacks to subsequent convective cloud development.


Equal Employment Opportunity

Battelle Memorial Institute (BMI) at Pacific Northwest National Laboratory (PNNL) is an Affirmative Action/Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All BMI staff must be able to demonstrate the legal right to work in the United States. BMI is an E-Verify employer. Learn more at jobs.pnnl.gov.


Minimum Qualifications

BS/BA with 2 years’ experience, MS/MA with 0 years’ experience, PhD with 0 years’ experience

o Technical Expertise: Contributing professional who is building a professional reputation for technical expertise. Fully applies and interprets standard theories, principles, methods, tools and technologies within specialty.
o Level of Responsibility: Independently completes recurring assignments. Exercises limited judgment on work details. Makes preliminary selections and adaptations of technical alternatives.
o Breadth of Technical Knowledge: Continues developing technical expertise and knowledge. Develops new skills.


Preferred Qualifications

PhD 2 years’ experience.

Preference will be given to candidates with:
o Experience in understanding, modifying, debugging, and running code for cloud-system resolving models (e.g., WRF)
o Experience analyzing cloud/precipitation measurements, notably from radars (e.g., understanding radar measurements and using radar software such as Py-ART), satellites (e.g. GOES, MODIS, TRMM), and cloud-resolving (LES) and cloud-system resolving models
o Familiarity with high performance computing, and statistical analysis
o Expertise in convective meteorology (e.g., relevant environmental conditions and nature of convective dynamics and microphysics)
o Experience with analysis of large datasets and efficient computation, including parallel processing and machine learning.
o Experience in data assimilation techniques and practical application in mesoscale models
o Experience with computational environments (Linux/Unix), computer languages (Fortran, Python, etc.), and analysis tools (R, NCL, Matlab, IDL, etc.) usually used for atmospheric modeling
o Strong communication skills


Organization and Job ID

Job ID: 308075
Directorate: Earth and Biological Sciences
Division: Atmospheric Sciences and Global Change
Group: Atmospheric Measurement and Data Sciences